Discrete wavelet transform based seizure detection in newborns EEG signals

Pega Zarjam, Mostefa Mesbah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

This paper proposes a novel method for detecting newborns seizure events from electroencephalogram (EEG) data. The detection scheme is based on the discrete wavelet transform (DWT) of the EEG signals. The number of zero-crossings, the average distance between adjacent zero-crossings, the number of extrema, and the average distance between adjacent extrema of the wavelet coefficients (WCs) of certain scales are extracted to form a feature set. The extracted feature set is then fed to an artificial neural network (ANN) classifier to organize the EEG signals into seizure and non- seizure activities. In this study, the training and test sets were obtained from EEG data acquired from 1 and 5 other neonates, respectively, with ages ranging from 2 days to 2 weeks. The obtained results show that on the average 95% of the EEG seizures were detected by the proposed scheme.

Original languageEnglish
Title of host publicationProceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
PublisherIEEE Computer Society
Pages459-462
Number of pages4
Volume2
ISBN (Print)0780379462, 9780780379466
DOIs
Publication statusPublished - 2003
Event7th International Symposium on Signal Processing and Its Applications, ISSPA 2003 - Paris, France
Duration: Jul 1 2003Jul 4 2003

Other

Other7th International Symposium on Signal Processing and Its Applications, ISSPA 2003
CountryFrance
CityParis
Period7/1/037/4/03

ASJC Scopus subject areas

  • Signal Processing

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  • Cite this

    Zarjam, P., & Mesbah, M. (2003). Discrete wavelet transform based seizure detection in newborns EEG signals. In Proceedings - 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003 (Vol. 2, pp. 459-462). [1224913] IEEE Computer Society. https://doi.org/10.1109/ISSPA.2003.1224913